Figures 3
rstudio
September 10, 2024 10:14:51 CEST
knitr::opts_chunk$set(
echo = TRUE,
message = FALSE,
warning = FALSE,
dev = c("png", "jpeg", "pdf")
)Introduction
Load packages
Open corrected counts and annotated Seurat object
Load the corrected (SoupX), normalized
(SCTransformed) and annotated (Twice mapped - Tabula
Sapiens Skin reference and PBMC azimuth reference) data.
srat <- readRDS( "20241005_Ressler2024_NeoBCC.Rds")
meta <- srat@meta.data
meta$WHO <- "SD"
meta$WHO[meta$patient %in% c("NeoBCC007_post", "NeoBCC008_post", "NeoBCC012_post", "NeoBCC017_post")] <- "CR"
meta$WHO[meta$patient %in% c("NeoBCC004_post", "NeoBCC006_post", "NeoBCC010_post", "NeoBCC011_post")] <- "PR"
srat <- AddMetaData(srat, meta$WHO, col.name = "WHO")
srat$WHO <- factor(srat$WHO, levels = c("CR", "PR", "SD"))Define levels and colors
srat@meta.data$anno_l1 <- factor(srat@meta.data$anno_l1, levels=c("other",
"Mast cells",
"Mono-Mac",
"LC",
"DC",
"pDC",
"Plasma cells",
"B cells" ,
"Proliferating cells",
"Natural killer cells",
"CD8+ T cells",
"Tregs",
"CD4+ T cells" ,
"Melanocytes",
"Endothelial cells",
"Fibroblasts",
"Keratinocytes",
"Malignant cells"))
colors <- c("Malignant cells" = "#bd0026",
"Keratinocytes" = "#dfc27d",
"Fibroblasts" = "#f6e8c3",
"Endothelial cells" = "#54278f",
"Melanocytes" = "#a65628",
"CD4+ T cells" = "#b8e186",
"Tregs" = "#ae017e",
"CD8+ T cells" = "#fbb4ae",
"Proliferating cells" = "#b3cde3",
"Natural killer cells" = "#9e9ac8",
"B cells" = "#7bccc4",
"Plasma cells" = "#35978f",
"pDC" = "#fe9929",
"DC" = "#e7298a",
"LC" = "yellow" ,
"Mono-Mac" = "#fec44f",
"Mast cells" = "#bf812d",
"other" = "#bdbdbd")Figure 3a
p <- SCpubr::do_DimPlot(sample = srat,
colors.use = colors,
group.by = "anno_l1",
pt.size=0.5, label = TRUE,
repel = TRUE,
legend.position = "none",
label.color = "black") +
theme_minimal() +
NoLegend() +
theme(text = element_text(size=20))
p
Figure 3b
genes <- list(
"Mal." = c("KRT17", "EPCAM", "BCAM"),
"Kerati." = c("FGFBP1", "KRT1", "KRT6A"),
"Fibro." = c("COL1A1", "COL1A2", "COL6A2"),
"E" = c("VWF"),
"Mel" = c("MLANA", "PMEL"),
"CD4+T" = c("CD3E","CD2", "CD4" ),
"Tregs" = c("IL2RA", "CD25", "FOXP3", "TNFRSF4"),
"CD8+T" = c("CD8A", "GZMA"),
"NK" = c( "KLRC1", "PRF1", "GNLY"),
"P" = c("MKI67"),
"B" = c("MS4A1", "CD19"),
"Plasma" = c("IGKC", "CD38", "SDC1"),
"pDC" = c( "IRF8", "CLEC4C"),
"DC" = c("LAMP3", "CCR7"),
"LC" = c("CD1A", "CD207"),
"Mono-Mac" = c("CD68", "CD14" ),
"Mast" = c("KIT", "SOCS1"))
p <- SCpubr::do_DotPlot(sample = srat,
features = genes,
group.by = "anno_l1",
font.size = 25,
legend.length = 4,
legend.type = "colorbar",
dot.scale = 8,
colors.use = c("#7fbc41","#b8e186", "#f7f7f7","#fde0ef", "#c51b7d"))
p
Not included in the final version of the manuscript
# Subset immune / non-immune cells
imm <- subset(srat, subset = anno_l1 %in% c("other",
"Mast cells",
"Mono-Mac",
"LC",
"DC",
"pDC",
"Plasma cells",
"B cells" ,
"Proliferating cells",
"Natural killer cells",
"CD8+ T cells",
"Tregs",
"CD4+ T cells" ))
other <- subset(srat,subset = anno_l1 %in% c("Malignant cells",
"Keratinocytes",
"Fibroblasts",
"Endothelial cells",
"Melanocytes"))
imm$anno_l1 <- droplevels(imm$anno_l1)
other$anno_l1 <- droplevels(other$anno_l1)
# subset pCR and nonpCR for each subset
pCR_imm <- subset(imm, subset = Pathological.Response == "pCR" )
nonpCR_imm <- subset(imm, subset = Pathological.Response == "non-pCR")
pCR_other <- subset(other, subset = Pathological.Response == "pCR" )
nonpCR_other <- subset(other, subset = Pathological.Response == "non-pCR")
### plots
d1 <- SCpubr::do_BarPlot(pCR_other,
group.by = "anno_l1",
split.by = "patient",
position = "fill",
order=FALSE,
legend.position = "none",
flip = TRUE,
font.size = 14,
colors.use = colors,
ylab = "CD45- EPCAM+")
d2 <- SCpubr::do_BarPlot(nonpCR_other,
group.by = "anno_l1",
split.by = "patient",
position = "fill",
order=FALSE,
legend.position = "none",
flip = TRUE,
font.size = 14,
colors.use = colors,
ylab = "CD45- EPCAM+")
d3 <- SCpubr::do_BarPlot(pCR_imm,
group.by = "anno_l1",
split.by = "patient",
position = "fill",
order=FALSE,
legend.position = "none",
flip = TRUE,
font.size = 20,
colors.use = colors,
ylab = "CD45+ EPCAM-")
d4 <- SCpubr::do_BarPlot(nonpCR_imm,
group.by = "anno_l1",
split.by = "patient",
position = "fill",
order=FALSE,
legend.position = "none",
flip = TRUE,
font.size = 20,
colors.use = colors,
ylab = "CD45+ EPCAM-")
p <- d3 + d4 + plot_layout(ncol = 1, heights = c(1,2))
p
Session Info
## R version 4.3.0 (2023-04-21)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.4 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8
## [6] LC_MESSAGES=en_US.UTF-8 LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: Europe/Vienna
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggpubr_0.6.0.999 ggplot2_3.5.1 viridis_0.6.5 viridisLite_0.4.2 reshape2_1.4.4 tibble_3.2.1 SCpubr_2.0.2
## [8] DT_0.32 patchwork_1.2.0 dplyr_1.1.4 Seurat_5.0.3 SeuratObject_5.0.1 sp_2.1-3
##
## loaded via a namespace (and not attached):
## [1] RColorBrewer_1.1-3 rstudioapi_0.16.0 jsonlite_1.8.8 magrittr_2.0.3 spatstat.utils_3.0-4 farver_2.1.2
## [7] rmarkdown_2.26 fs_1.6.4 DElegate_1.2.1 vctrs_0.6.5 ROCR_1.0-11 memoise_2.0.1
## [13] spatstat.explore_3.2-7 rstatix_0.7.2 forcats_1.0.0 htmltools_0.5.8 broom_1.0.5 gridGraphics_0.5-1
## [19] sass_0.4.9 sctransform_0.4.1 parallelly_1.37.1 bslib_0.6.2 KernSmooth_2.23-22 htmlwidgets_1.6.4
## [25] ica_1.0-3 plyr_1.8.9 cachem_1.1.0 plotly_4.10.4 zoo_1.8-12 igraph_2.0.3
## [31] mime_0.12 lifecycle_1.0.4 pkgconfig_2.0.3 Matrix_1.6-5 R6_2.5.1 fastmap_1.2.0
## [37] fitdistrplus_1.1-11 future_1.33.2 shiny_1.8.1 digest_0.6.35 colorspace_2.1-0 tensor_1.5
## [43] RSpectra_0.16-1 irlba_2.3.5.1 crosstalk_1.2.1 labeling_0.4.3 progressr_0.14.0 fansi_1.0.6
## [49] spatstat.sparse_3.0-3 httr_1.4.7 polyclip_1.10-6 abind_1.4-5 compiler_4.3.0 withr_3.0.0
## [55] backports_1.4.1 carData_3.0-5 DBI_1.2.2 fastDummies_1.7.3 highr_0.10 ggsignif_0.6.4
## [61] MASS_7.3-60.0.1 tools_4.3.0 lmtest_0.9-40 httpuv_1.6.15 future.apply_1.11.1 goftest_1.2-3
## [67] glue_1.7.0 nlme_3.1-164 promises_1.2.1 grid_4.3.0 Rtsne_0.17 cluster_2.1.6
## [73] generics_0.1.3 gtable_0.3.5 spatstat.data_3.0-4 tidyr_1.3.1 data.table_1.15.2 car_3.1-2
## [79] utf8_1.2.4 spatstat.geom_3.2-9 RcppAnnoy_0.0.22 ggrepel_0.9.5 RANN_2.6.1 pillar_1.9.0
## [85] stringr_1.5.1 yulab.utils_0.1.4 spam_2.10-0 RcppHNSW_0.6.0 later_1.3.2 splines_4.3.0
## [91] lattice_0.22-6 survival_3.5-8 deldir_2.0-4 tidyselect_1.2.1 miniUI_0.1.1.1 pbapply_1.7-2
## [97] knitr_1.45 gridExtra_2.3 scattermore_1.2 xfun_0.43 matrixStats_1.2.0 stringi_1.8.4
## [103] lazyeval_0.2.2 yaml_2.3.8 evaluate_0.23 codetools_0.2-19 ggplotify_0.1.2 cli_3.6.2
## [109] uwot_0.1.16 xtable_1.8-4 reticulate_1.35.0 jquerylib_0.1.4 munsell_0.5.1 Rcpp_1.0.12
## [115] globals_0.16.3 spatstat.random_3.2-3 dbplyr_2.5.0 png_0.1-8 parallel_4.3.0 assertthat_0.2.1
## [121] dotCall64_1.1-1 listenv_0.9.1 scales_1.3.0 ggridges_0.5.6 crayon_1.5.2 leiden_0.4.3.1
## [127] purrr_1.0.2 rlang_1.1.4 cowplot_1.1.3